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Fundamentals of Infrared Thermal Imaging
Published in U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer, Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications, 2023
U. Snekhalatha, K. Palani Thanaraj, Kurt Ammer
Jakkaw and Onoye (2020) monitored the respiratory activity and body movements during the sleep using the thermal imaging technique. The breathing patterns are detected during sleeping and identifying the sleep disorders such as sleep apnea caused due to hypertension, cardiovascular diseases, and arrhythmia. Hence, the authors demonstrated the non-contact method of respiratory and body movement detection using a thermal camera which detects the temperature changes due to breathing patterns. The thermal video frames of subjects in sleeping mode are considered the input images. The images are pre-processed using the Gaussian filter. In the respiration-monitoring method, ROIs are automatically detected from the input images by means of identifying the highest temperature point and massive portions of the high-temperature area. For automated ROI detection of the thermal image, sleeping position is considered. The highest temperature points are detected in the image by using minimum and maximum intensities found in the image. The maximum pixel intensities are associated with the highest temperature of the body. After determining the pixel at the center of the observation area, a rectangular ROI of pixel size either 10 × 10, 25 × 25, or 50 × 50 is applied. Among the ROI used, empirical research result shows that 50 × 50 pixels produced very good accuracy in compliance with an original frame 640 × 480. The massive portions of the high-temperature area are detected using the thresholding method. A threshold value of 176 was set, which produced best results in segregating the human skin area from the background.
The Internet of Medical Things for Monitoring Health
Published in Bharat Bhushan, Sudhir Kumar Sharma, Bhuvan Unhelkar, Muhammad Fazal Ijaz, Lamia Karim, Internet of Things, 2022
Rehab A. Rayan, Christos Tsagkaris, Andreas S. Papazoglou, Dimitrios V. Moysidis
In recent years, it has been proved that appropriate home care has a significant contribution in preventing complicated sleep apnea, which includes hypertension and coronary heart disease. Home care devices can serve as a reliable monitoring modality, detecting exacerbations that require a reassessment of the therapeutic approach. The automatic portable sleep apnea detector of this study seems to be a noninvasive, low cost, and universally accessible solution. The algorithms of the device were assessed with the St. Vincent's University Hospital/University College Dublin sleep apnea dataset. Besides, the user data are collected from 10 apnea conditions. The function of the suggested algorithm reached a mean precision, specificity, and sensitivity of 98.54%, 98.95%, and 97.05% respectively (Haoyu et al. 2019).
A study of poultry realtime monitoring and automation techniques
Published in Arun Kumar Sinha, John Pradeep Darsy, Computer-Aided Developments: Electronics and Communication, 2019
A. Arun Gnana Raj, S. Margaret Amala, J. Gnana Jayanthi
Sungho Kaneshiro and Yasue Mitsukura [10] has proposed respiratory sound analysis for Continuous Positive Airway Pressure Machines. Continuous positive airway pressure (CPAP) is a medical treatment for obstructive sleep apnoea syndrome. For CPAP, it is necessary to monitor the respiratory sounds such as, inspiration, expiration, and snoring to supply proper pressure depending on the respiratory conditions of patients. As the first step, short-time Fourier transform is applied to each channel of observed microphone signals for selectively acquiring directional signals and then for extracting time-frequency (T-F) features. Different features can be extracted in the time-frequency magnitude spectrogram. Now compare the sound features of inspiration and expiration produced by the microphones, 50 Hz notch filter has been applied and the data will be divided into epoch. For separate respiratory sounds and CPAP machine noise the degenerate un-mixing estimation technique (DUET) has been used. The experiments clearly shows that the amplitude and frequency bands are different between the inspiration and expiration states. So that the inspiration and expiration can be easily identified.
Speed and Accuracy Trade-off ANN/SVM Based Sleep Apnea Detection with FPGA Implementation
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Talal Bonny, Mahmmud Qatmh, Khaled Obaideen, Maryam Nooman AlMallahi, Mohammad Al-Shabi, Ahmed Al-Shammaa
Recent research has revealed promising results in the application of deep learning, embedded systems, and FPGA implementation (Bonny and Henkel 2007, 2008, 2010, 2022), particularly in the diagnosis and treatment of sleep apnoea (Nausheen et al. 2018; Sambas et al. 2021; Aiyer et al. 2022; Karnati et al. 2022; Sahu et al. 2022, 2023; Baba and Bonny 2023). Sleep apnoea is a common sleep disorder that can lead to a range of health problems, including high blood pressure, heart disease, and stroke, due to interrupted breathing during sleep. The current diagnostic process involves an overnight sleep study in a specialised clinic, which can be costly and inconvenient for patients. However, the use of deep learning algorithms implemented on FPGAs has shown that sleep apnoea can be accurately diagnosed using simple sensors, such as a smartphone or wearable device. By analysing signals such as heart rate and breathing patterns, these algorithms can detect episodes of sleep apnoea and estimate their severity, providing a more accessible and affordable solution for diagnosis and monitoring. Additionally, deep learning algorithms can assist in the optimisation of therapy for sleep apnoea, improving patient outcomes. The integration of deep learning and FPGA implementation in the diagnosis and treatment of sleep apnoea has the potential to reduce healthcare costs while improving patient outcomes. It is an exciting area of research that promises significant benefits for the healthcare industry.
Multi-dimensional readiness assessment of medical devices
Published in Theoretical Issues in Ergonomics Science, 2023
Rosemary Ruiz Seva, Angela Li Sin Tan, Lourdes Marie Sequerra Tejero, Maria Lourdes Dorothy S. Salvacion
The lack of a usability study can result in technical glitches and patient mortality as in the Therac-25 accidents with patients suffering severe radiation burns (Leveson 1995; Leveson and Turner 1993). Some MDs are designed to be used alone by the patient at home or with the aid of a caregiver. A case in point is the Continuous Positive Airway Pressure (CPAP) machine used for sleep apnea, a potentially serious disorder. The correct setup of the device and proper fitting of the mask on the face is important to ensure that the MD will deliver the continuous positive pressure to the airways for the entire duration of sleep. A usability study conducted on this MD showed that patients find it difficult to get it ready for use, and caregivers find it hard to maintain (Fung et al. 2015).
An automated method for sleep apnoea detection using HRV
Published in Journal of Medical Engineering & Technology, 2022
Obstructive sleep apnoea (OSA) is a common sleep-disordered breathing disorder characterised by cessation of breathing or shortness of breath within 10 s. Shortness of breath can be caused by not getting enough oxygen to the brain and other body parts [1]. In some cases, the apnoea period may last more than 30 s, which can even be fatal. 14% of men and 5% of women in the United States have obstructive sleep apnoea. Some patients may experience 300 attacks of sleep apnoea overnight [2]. If left untreated, sleep apnoea can cause serious health problems, including high blood pressure, stroke, heart failure, irregular heartbeats, and heart attacks, diabetes, depression, headaches, and attention deficit hyperactivity disorder [3]. So far, several studies have been conducted by researchers to diagnose obstructive sleep apnoea. The following are several methods in this area.